In testing hypotheses, we may confront with cases where data are recorded as non-precise (fuzzy) rather than crisp. In such situations, the classical methods of testing hypotheses are not capable and need to be generalized. In solving the problem of testing hypotheses based on fuzzy data, the fuzziness of the observed data leads to the fuzzy p-value. This paper has been focused to calculate fuz...

This paper reviews and compares two R packages ``FPV" and ``Fuzzy.p.value".These packages are designed for testing hypotheses in a fuzzy environment using a fuzzy $p$-value based approach.In fact, the packages ``FPV" and ``Fuzzy.p.value" propose some useful functions for testing hypotheses when the data / hypotheses are fuzzy rather than crisp.The proposed methods and function...

We introduce a neutral statistic S that makes the Conditional Frequentist error reports identical to Bayesian posterior probabilities of the hypotheses. In symmetrical cases we can show this strategy to be optimal from the Frequentist perspective. A Conditional Frequentist who uses such a strategy can exploit the consistency of the method with the Likelihood Principle—for example, the validity ...

Predictive developmental hypotheses play a crucial role in developmental theories. These hypotheses link early developmental behaviors or processes to later developmental outcomes. Empirical tests of predictive developmental hypotheses are generally based on standard regression models. It is argued that hierarchical linear models or longitudinal multilevel models offer a better alternative. A m...